Designers of imaging systems often assess the performance of their designs in terms of physical parameters such as contrast, resolution and bit-rate efficiency in compression/decompression (codec) processes. While these parameters can be easily measured, they may not be accurate gauges for evaluating performance. The reason is that end users of imaging systems are generally more concerned with the subjective visual performance such as the visibility of artifacts or distortions and in some cases, the enhancement of these image features which may reveal information such as the existence of a tumor in an image, e.g., a MRI (Magnetic Resonance Imaging) image or a CAT (Computer-Assisted Tomography) scan image.
For example, an input image can be processed using two different codec algorithms to produce two different codec images. If the measure of codec image fidelity is based purely on parameters such as performing mean squared error (MSE) calculations on both codec images without considering the psychophysical properties of human vision, the codec image with a lower MSE value may actually contain more noticeable distortions than that of a codec image with a higher MSE value.
Over the years, various human visual performance methods (perceptual metric generator or visual discrimination measure) have been used to improve imaging system design. These visual discrimination measure can be broadly classified as "spatial" or "spatiotemporal". Examples of spatial visual discrimination measures include the Carlson and Cohen generator and the square root integral (SQRI) generator. Examples of a spatiotemporal visual discrimination measures (VDM) are disclosed in U.S. Pat. application Ser. No. 08/668,015, filed Jun. 17, 1996 and "Method And Apparatus For Assessing The Visibility Of Differences Between Two Image Sequences" filed on Mar. 28, 1997 with docket number DSRC12146.
However, visual discrimination measures are often required to quickly generate (e.g., in real-time) a perceptual metric (fidelity metric) which is then used to visually optimize some other processes, e.g., encoding applications.
Therefore, a need exists in the art for an architecture and concomitant method that quickly performs the calculations required of the perceptual metric generator. For the device disclosed in DSRC12146, one requirement is to decompose an image stream into units of contrast localized in spatial and temporal frequency.